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ABSTRACT A robust, rapid, and environmentally conscious ultrahigh‐performance liquid chromatography (UHPLC) method was developed and validated for the quantification of favipiravir and its related substances—favipiravir‐D4 (internal standard) and favipiravir‐5‐hydroxy—in pharmaceutical formulations. Method development was guided by a quality‐by‐design (QbD) framework, utilizing response surface methodology to model the effects of critical variables—ethanol concentration, mobile phase pH, and flow rate—on chromatographic responses including retention time (RT), tailing factor (TF), and theoretical plates (TP). Optimal separation was achieved with 20% ethanol, 0.2 mL/min flow rate, and pH 2.8, delivering high resolution and efficiency. Validation was performed according to ICH Q2(R1) guidelines, demonstrating excellent linearity ( R 2 > 0.999), high sensitivity with LOQ at 0.05%, accuracy with recoveries averaging 99.2%, and precision with %RSD consistently below 2%. Robustness testing confirmed method reliability under intentional variations of critical parameters. Forced degradation studies—including acidic, alkaline, oxidative, thermal, photolytic, and hydrolytic stress—confirmed the stability‐indicating capability, with complete separation of favipiravir from its degradation products. The method was successfully applied to quantify favipiravir in a marketed formulation (Avigan), confirming its suitability for routine quality control and stability testing. Environmental assessments using Complementary Green Analytical Procedure Index (ComplexGAPI), Analytical GREEnness Metric (AGREE), AGREEprep, Analytical Eco‐Scale, and Blue Applicability Grade Index (BAGI) (scores > 0.70) demonstrated minimal reagent use, operational efficiency, and green analytical performance. Overall, the developed UHPLC method provides a sustainable, high‐throughput, and regulatory‐compliant platform for routine pharmaceutical analysis, impurity profiling, and stability studies, exemplifying the integration of green chemistry principles with advanced method optimization in modern analytical workflows.
Chagarlamudi et al. (Fri,) studied this question.
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